---
title: LSPW Periodic Workflow Discovery
emoji: 🔄
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: "6.2.0"
app_file: app.py
pinned: false
---

# Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities

[Project Page](https://sites.google.com/view/periodicworkflow) | [arXiv](https://www.arxiv.org/abs/2511.14945)

## Abstract

Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities—characterized by simple structures and high-contrast patterns—have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored.

To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns.


## Usage

### Dependencies
Ensure you have the following Python packages installed:
- `numpy`
- `scikit-learn`
- `tqdm`
- `matplotlib`
- `scipy`

You can install them using pip:
```bash
pip install numpy scikit-learn tqdm matplotlib scipy
```

### Estimation
Run the workflow detection function to perform unsupervised periodic workflow detection on the dataset.